BRAINA JOURNAL OF NEUROLOGY

Divergent network connectivity changesin behavioural variant frontotemporaldementia and Alzheimer’s diseaseJuan Zhou,1 Michael D. Greicius,2 Efstathios D. Gennatas,1 Matthew E. Growdon,1 Jung Y. Jang,1

Gil D. Rabinovici,1 Joel H. Kramer,1 Michael Weiner,3 Bruce L. Miller1 and William W. Seeley1

1 Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA 94117, USA

2 Functional Imaging in Neuropsychiatric Disorders (FIND) Lab, Department of Neurology and Neurological Sciences, Stanford University School

of Medicine, Stanford, CA 94305, USA

3 Center for Imaging of Neurodegenerative Diseases, Department of Veterans Affairs Medical Center, San Francisco, CA 94121, USA

Correspondence to: William W. Seeley,

Box 1207, UCSF,

San Francisco,

CA 94143-1207, USA

E-mail: wseeley@memory.ucsf.edu

Resting-state or intrinsic connectivity network functional magnetic resonance imaging provides a new tool for mapping

large-scale neural network function and dysfunction. Recently, we showed that behavioural variant frontotemporal dementia

and Alzheimer’s disease cause atrophy within two major networks, an anterior ‘Salience Network’ (atrophied in behavioural

variant frontotemporal dementia) and a posterior ‘Default Mode Network’ (atrophied in Alzheimer’s disease). These networks

exhibit an anti-correlated relationship with each other in the healthy brain. The two diseases also feature divergent

symptom-deficit profiles, with behavioural variant frontotemporal dementia undermining social-emotional function and preser-

ving or enhancing visuospatial skills, and Alzheimer’s disease showing the inverse pattern. We hypothesized that these dis-

orders would exert opposing connectivity effects within the Salience Network (disrupted in behavioural variant frontotemporal

dementia but enhanced in Alzheimer’s disease) and the Default Mode Network (disrupted in Alzheimer’s disease but enhanced

in behavioural variant frontotemporal dementia). With task-free functional magnetic resonance imaging, we tested these ideas

in behavioural variant frontotemporal dementia, Alzheimer’s disease and healthy age-matched controls (n = 12 per group), using

independent component analyses to generate group-level network contrasts. As predicted, behavioural variant frontotemporal

dementia attenuated Salience Network connectivity, most notably in frontoinsular, cingulate, striatal, thalamic and brainstem

nodes, but enhanced connectivity within the Default Mode Network. Alzheimer’s disease, in contrast, reduced Default Mode

Network connectivity to posterior hippocampus, medial cingulo-parieto-occipital regions and the dorsal raphe nucleus, but

intensified Salience Network connectivity. Specific regions of connectivity disruption within each targeted network predicted

intrinsic connectivity enhancement within the reciprocal network. In behavioural variant frontotemporal dementia, clinical se-

verity correlated with loss of right frontoinsular Salience Network connectivity and with biparietal Default Mode Network

connectivity enhancement. Based on these results, we explored whether a combined index of Salience Network and Default

Mode Network connectivity might discriminate between the three groups. Linear discriminant analysis achieved 92% clinical

classification accuracy, including 100% separation of behavioural variant frontotemporal dementia and Alzheimer’s disease.

Patients whose clinical diagnoses were supported by molecular imaging, genetics, or pathology showed 100% separation using

this method, including four diagnostically equivocal ‘test’ patients not used to train the algorithm. Overall, the findings suggest

that behavioural variant frontotemporal dementia and Alzheimer’s disease lead to divergent network connectivity patterns,

doi:10.1093/brain/awq075 Brain 2010: 133; 1352–1367 | 1352

Received November 13, 2009. Revised February 22, 2010. Accepted February 26, 2010

� The Author (2010). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved.

For Permissions, please email: journals.permissions@oxfordjournals.org

consistent with known reciprocal network interactions and the strength and deficit profiles of the two disorders. Further

developed, intrinsic connectivity network signatures may provide simple, inexpensive, and non-invasive biomarkers for dementia

differential diagnosis and disease monitoring.

Keywords: functional magnetic resonance imaging; frontotemporal dementia; Alzheimer’s disease; functional connectivity;biomarker

Abbreviations: bvFTD = behavioural variant frontotemporal dementia; CDR-SB = Clinical Dementia Rating, sum of boxes score;DMN = Default Mode Network; fMRI = functional magnetic resonance imaging; ICA = independent component analysis;ICN = Intrinsic connectivity network; PIB = Pittsburgh compound B; SFVAMC = San Francisco Veterans Affairs Medical Center

IntroductionNeurodegenerative diseases target specific neuronal populations

within large-scale distributed networks. In early stage disease,

region-specific synapse loss, neurite retraction, and gliosis precede

neuron loss (Brun et al., 1995) causing neural system dysfunction

and symptoms that may anticipate MRI-detectable atrophy.

Resting-state or intrinsic connectivity network (ICN) functional

magnetic resonance imaging (fMRI) provides a novel tool with

the potential to detect disease-related network alterations before

brain atrophy has emerged. Furthermore, because cognitive and

behavioural functions rely on large-scale network interactions

(Mesulam, 1998), ICN fMRI may clarify fundamental aspects of

disease pathophysiology. The ICN technique maps temporally

synchronous, spatially distributed, spontaneous low frequency

(50.08 Hz) blood-oxygen level-dependent signal fluctuations at

rest or, more accurately, in task-free settings (Fox and Raichle,

2007). To date, ICN fMRI has been used to chart normal

human and monkey cortical network architecture (Greicius et al.,

2003; Beckmann et al., 2005; Fox et al., 2005; Salvador et al.,

2005; Damoiseaux et al., 2006; Seeley et al., 2007b; Vincent

et al., 2007), predict individual differences in human behaviour

and cognition (Hampson et al., 2006; Seeley et al., 2007b;

Di Martino et al., 2009b), and confirm that spatial atrophy

patterns in five distinct neurodegenerative syndromes mirror

normal human ICNs (Seeley et al., 2009). Testing patients directly,

ICN analysis has detected predictable connectivity reduction in

Alzheimer’s disease (Greicius et al., 2004; Rombouts et al.,

2005; He et al., 2007; Supekar et al., 2008; Fleisher et al.,

2009), prodromal Alzheimer’s disease (Rombouts et al., 2005;

Sorg et al., 2007), asymptomatic individuals at risk for

Alzheimer’s disease (Filippini et al., 2009), amyotrophic lateral

sclerosis (Mohammadi et al., 2009) and Parkinson’s disease

(Helmich et al., 2009; Wu et al., 2009); but this technique has

not been applied to patients with any frontotemporal dementia

syndrome or used to differentiate one disease from another.

Behavioural variant frontotemporal dementia (bvFTD) and

Alzheimer’s disease, the two most common causes of dementia

among patients less than 65 years of age (Ratnavalli et al., 2002),

provide a robust conceptual framework for exploring ICN fMRI ap-

plications to neurodegenerative disease. Early bvFTD disrupts com-

plex social-emotional functions that rely on anterior peri-allocortical

structures, including the anterior cingulate cortex and frontoinsula,

as well as the amygdala and striatum (Rosen et al., 2002; Broe

et al., 2003; Boccardi et al., 2005; Seeley et al., 2008a). These

regions constitute a large-scale ICN in healthy subjects, which we

have referred to as the ‘Salience Network’ due to its consistent

activation in response to emotionally significant internal and

external stimuli (Seeley et al., 2007b). Notably, while this anterior

network degenerates, posterior cortical functions survive or even

thrive, at times associated with emergent visual creativity (Miller

et al., 1998; Seeley et al., 2008b). In contrast, Alzheimer’s disease

often preserves social-emotional functioning, damaging instead a

posterior hippocampal-cingulo-temporal-parietal network, often

referred to as the ‘Default Mode Network’ (DMN) (Raichle et al.,

2001; Greicius et al., 2003; Buckner et al., 2005; Seeley et al.,

2009). DMN-specific functions continue to stir debate, but elem-

ents of this system, especially its posterior cortical nodes, participate

in episodic memory (Zysset et al., 2002; Buckner et al., 2005) and

visuospatial imagery (Cavanna and Trimble, 2006); functions lost

early in Alzheimer’s disease. Just as bvFTD and Alzheimer’s disease

show opposing clinical strengths and weaknesses, the Salience

Network and DMN show anticorrelated ICN time series (Greicius

et al., 2003; Fox et al., 2005; Fransson, 2005; Seeley et al., 2007b),

suggesting a reciprocal relationship between these two neural

systems. This rich clinical and neuroimaging background led us to

hypothesize (as detailed in Seeley et al., 2007a) that bvFTD and

Alzheimer’s disease would exert opposing influences on the Salience

Network and DMN.

In this study, we used task-free ICN fMRI to demonstrate a

divergent effect of bvFTD and Alzheimer’s disease on core

neural network dynamics. The results provide new insights into

the pathophysiology of these disorders and highlight the potential

of ICN mapping to provide clinically useful neurodegenerative

disease biomarkers.

Materials and methodsFigure 1 provides a schematic summary of the study design and

our motivating hypotheses.

SubjectsAll subjects (or their surrogates) provided informed consent according

to the Declaration of Helsinki and the procedures were approved

by the Institutional Review Boards at the University of California,

San Francisco (UCSF) and Stanford University. Patients were recruited

through the UCSF Memory and Aging Center, where all underwent

a comprehensive neurological, neuropsychological and functional

Divergent connectivity in bvFTD and Alzheimer’s disease Brain 2010: 133; 1352–1367 | 1353

assessment. Final diagnoses were rendered at a multidisciplinary

consensus conference, as detailed previously (Rosen et al., 2002). To

be considered for inclusion, patients were required to meet published

research criteria, which do not include neuroimaging features, for

probable Alzheimer’s disease (McKhann et al., 1984) and bvFTD

(Neary et al., 1998), within 90 days of MRI scanning. In addition,

patients were required to have (i) Clinical Dementia Rating (CDR)

and Mini-Mental State Examination scores obtained within 180 days

of scanning and (ii) absence of significant vascular or other structural

lesions on MRI. Finally, because ICN MRI provides an index of brain

function that may depend partly on level of consciousness (Kiviniemi

et al., 2005), patients were required to tolerate the scanning session

without sedation. These requirements slowed bvFTD enrolment due to

the behavioural nature of the syndrome. Scanning began at Stanford

University but shifted, due to scheduling difficulties, to the San

Francisco Veterans Affairs Medical Center (SFVAMC), closer to the

primary clinical study site (UCSF). Therefore, we combined patients

scanned at the two sites, with five per group scanned at Stanford

and seven per group scanned at the SFVAMC to reach our target

enrolment of 12 subjects per group. Patients meeting inclusion criteria

were scanned as they became available. BvFTD was the last group to

reach target fMRI enrolment (n = 12). At that point, 12 patients with

Alzheimer’s disease (from 15 available) and 12 healthy controls (from

17 available) were selected to match, as closely as possible, the bvFTD

group for age, gender, education and handedness (Table 1). Healthy

control subjects were required to have a CDR total score of 0,

a Mini-Mental State Examination of 28 or higher, no significant history

of neurological disease or structural pathology on MRI, no

neuropsychiatric medications and a consensus diagnosis of cognitively

normal within 180 days of scanning. At the time of imaging, three

patients with Alzheimer’s disease were taking donepezil, and one of

these was also taking bupropion. Two patients with bvFTD were

taking fluoxetine, including one who was also taking risperidone.

Another two patients with bvFTD were taking donepezil, one of

whom was also taking duloxetine. No other subjects took neuro-

psychiatric medications. Medication changes (for example, donepezil

initiation in Alzheimer’s disease or discontinuation in bvFTD) often take

place after imaging at the clinical consensus conference. Because of

the diverse medication profiles in each group and the complete

confounding of medication with clinical status (patient versus control),

we elected not to model medication status in our analyses.

Because clinical syndromic diagnoses can lead to prediction errors

regarding underlying histopathology, we collected all available sup-

porting biological data on the patients in this series. These supporting

data were not used for subject selection, but were available for a

subset of patients selected according to the procedures described

above. Three patients with bvFTD had comorbid motor neuron

disease, which strongly supports an underlying diagnosis of fronto-

temporal lobar degeneration with transactivation response element

DNA binding protein of 43 kDa (TDP-43) inclusions (Hodges et al.,

2004). One of these patients died and showed frontotemporal lobar

degeneration with TDP-43, Type 2, at autopsy (Sampathu et al.,

2006). One other patient with bvFTD (without motor neuron disease)

later died of end-stage disease but did not undergo autopsy. All

patients with Alzheimer’s disease are living at time of writing.

Patients with bvFTD were screened for mutations in disease-causing

Figure 1 Study design schematic. Preprocessed task-free fMRI data were decomposed using ICA, and Salience Network (SN) and DMN

components were identified for each subject by calculating goodness-of-fit to network templates derived from an independent dataset of

young healthy controls. Grey matter maps were also derived from structural MRI data of each subject for atrophy correction. Hypotheses

regarding between-group connectivity alterations were tested using the linear contrasts shown. SN and DMN scores and the combined SN

minus DMN index for each subject were derived and entered into three-class linear discriminant analyses. HC = healthy controls;

AD = Alzheimer’s disease; VBM = voxel-based morphometry; LDA = linear discriminant analysis.

1354 | Brain 2010: 133; 1352–1367 J. Zhou et al.

genes according to patient wishes, clinical suspicion and availability of

these assays at the time of assessment. One patient with bvFTD was

found to harbour a mutation in the progranulin gene. No other

disease-causing mutations were identified among the nine patients

with bvFTD tested for progranulin or the four patients with bvFTD

tested for microtubule associated protein tau mutations. Out of

36 subjects, 9 (four with bvFTD, five with Alzheimer’s disease) under-

went PET imaging with the amyloid-b ligand Pittsburgh compound B

(PIB), following previous methods (Rabinovici et al., 2007a). All five

patients with Alzheimer’s disease were classified as ‘PIB-positive’, and

all four with bvFTD were classified as ‘PIB-negative’ based on visual

rating blinded to clinical diagnosis (Rabinovici et al., 2007a), and these

classifications were confirmed using a quantitative threshold for

PIB-positivity derived empirically from a contrast of patients with

Alzheimer’s disease and controls (Aizenstein et al., 2008).

Image acquisition

Structural imaging

Structural MRI scans of five controls, five Alzheimer’s disease and

five bvFTD subjects (Stanford fMRI subjects) were acquired at the

SFVAMC on a 1.5 Tesla Magneton VISION system (Siemens Inc.,

Iselin, NJ) using a standard quadrature head coil as previously

described (Seeley et al., 2008a). Briefly, a volumetric magnetization

prepared rapid gradient echo (MPRAGE) MRI (repetition time/echo

time/inversion time = 10/4/300 ms) sequence was used to obtain a

T1-weighted image of the entire brain (15� flip angle, 154 coronal

slices, matrix size 256�256, 1.0� 1.0 mm2 in-plane resolution of

1.5 mm slab thickness). Structural MRI scans of the remaining seven

controls, seven Alzheimer’s disease and seven bvFTD subjects

(SFVAMC fMRI subjects) were obtained on a Bruker MedSpec

4.0 Tesla whole body MRI system. A volumetric MPRAGE MRI (repe-

tition time/echo time = 2300/3.37 ms) sequence was used (7� flip

angle, 176 sagittal slices, matrix size 256�256, 1.0�1.0 mm2

in-plane resolution with a 1.0 mm slab thickness).

Functional imaging

Functional MRI scanning of 15 subjects (Stanford fMRI) was per-

formed at Stanford University. Images were acquired on a 3 Tesla

GE Signa Excite scanner (GE Medical Systems, Milwaukee, WI, USA)

using a standard GE whole head coil. Twenty-eight axial slices (4 mm

thick, 1 mm skip) parallel to the plane connecting the anterior and

posterior commissures and covering the whole brain were imaged

using a T2*-weighted gradient echo spiral pulse sequence (repetition

time/echo time = 2000/30 ms; 80� flip angle and 1 interleave). The

field of view was 200�200 mm2, and the matrix size was 64�64,

yielding an in-plane isotropic spatial resolution of 3.125 mm. To reduce

blurring and signal loss arising from field inhomogeneities, an auto-

mated high-order shimming method based on spiral acquisitions was

used before acquiring fMRI scans (Kim et al., 2000). All subjects

underwent a 6 min task-free fMRI scan after being instructed only to

remain awake with their eyes closed. Functional MRI scanning of the

remaining 21 subjects (SFVAMC fMRI) was performed at the SFVAMC

on a Bruker MedSpec 4.0 Tesla whole body MRI system. A total of 32

axial slices (3.5 mm thick) parallel to the plane connecting the anterior

and posterior commissures and covering the whole brain were imaged

using a T2*-weighted gradient echo spiral pulse sequence (repetition

time/echo time = 2500/30 ms; 90� flip angle and interleaved slicing).

Table 1 Subject demographic and neuropsychological features

Healthy controls bvFTD Alzheimer’sdisease

Overall ANOVA(F, df)

bvFTD/Alzheimer’sdisease

Age, years 62.0 (89.2) 60.8 (4.6) 63.3 (7.7) 0.4, 35 66.9 (9.9)

M:F, n 5:7 6:6 5:7 �2 = 0.3, 1 3:1

Handedness R:L, n 11:1 11:1 11:1 NA 4:0

Education, years 15.8 (3.1) 14.6 (2.2) 15.1 (3.9) 0.41, 35 15.8 (3.1)

Illness duration, years NA 3.9 (2.0) 4.2 (2.2) 0.18, 22 3.7 (1.3)

CDR, total 0.0 (0.0) 1.0 (0.4)h 1.0 (0.4)h 24.8, 31 1.1 (0.6)

CDR, sum of boxes 0.0 (0.0) 5.6 (2.2)h 5.4 (1.9)h 30.6, 31 6.5 (3.1)

MMSE (max = 30) 29.6 (0.7) 25.9 (4.0) 21.2 (5.1)hb 12.8, 33 25.3 (3.3)

CVLT-SF, four learning trials, total (max = 36) NC 22.5 (5.8) 17.5 (6.0) 4.0, 22 18.0 (7.3)

CVLT-SF, 10 min recall, score (max = 9) NC 4.0 (2.8) 1.1 (1.5)b 9.2, 22 2.0 (3.4)

Modified Rey-O copy (max = 17)* 15.6 (1.0) 14.4 (1.7) 9.3 (6.4)hb 6.5, 28 15.3 (1.3)

Modified Rey-O 10 min recall (max =17)* 11.6 (1.3) 7.4 (3.9)h 1.9 (2.2)hb 23.4, 28 3.8 (5.2)

Digit span backward* 5.4 (1.5) 3.9 (0.9)h 3.5 (1.4)h 5.4, 29 4.5 (1.9)

Modified trails (correct lines per minute)* 43.5 (20.2) 7.3 (9.9)h 19.2 (11.2)h 8.2, 29 15.2 (8.9)

Design fluency (correct designs per minute)* 13.5 (2.4) 6.3 (4.2)h 3.0 (2.4)h 12.5, 21 4.0 (2.9)

Letter fluency (‘D’ words in 1 min)* 16.6 (1.8) 9.3 (6.6)h 8.7 (4.8)h 5.7, 29 9.0 (3.8)

Semantic fluency (animals in 1 min)* 21.1 (4.9) 11.8 (5.6)h 8.9 (3.7)h 14.5, 29 10.3 (6.8)

Abbreviated BNT (max = 15) 14.3 (1.0) 12.3 (2.6) 10.8 (4.4) 2.5, 29 11.0 (3.6)

Calculations (max = 5)* 5.0 (0.0) 4.2 (0.8) 2.8 (1.5)hb 9.6, 29 3.8 (1.3)

NPI frequency� severity (max = 144) NC 36.5 (18.1)a 20.6 (23.1) 3.2, 20 46.0 (30.6)

NPI, caregiver distress sum (max = 60) NC 18.7 (10.5)a 9.7 (8.7) 4.3, 20 28.7 (20.6)

Values represent mean (SD). Superscript letters indicate whether group mean was significantly worse than healthy controls (h), Alzheimer’s disease (a) or bvFTD (b), basedon post hoc pairwise comparisons (P50.05). The far right column describes four patients with bvFTD versus Alzheimer’s disease who were not entered into the clinicalfeature ANOVA but were used to test the ICN diagnostic algorithm. Eight measures marked with asterisks were used for three-class classification in linear discriminantanalyses.BNT = Boston Naming Test; CDR = Clinical Dementia Rating; CVLT-SF = California Verbal Learning Test-Short form; MMSE = Mini-Mental State Examination; NA = not

applicable; NC = not collected; NPI = Neuropsychiatric Inventory.

Divergent connectivity in bvFTD and Alzheimer’s disease Brain 2010: 133; 1352–1367 | 1355

The field of view was 225�225 mm2, and the matrix size was

64�64, yielding an isotropic in-plane spatial resolution of 3.52 mm.

All subjects underwent a 7.5 min task-free fMRI scan after being

instructed only to remain awake with their eyes closed.

Balancing each group for scanner site allowed us to combine

subjects across sites within each group while minimizing scanner

confounding effects. Furthermore, a previous study showed that site

did not play a significant role in explaining the variance in a large

task-based fMRI dataset compared to individual variability (Sutton

et al., 2008). Nonetheless, especially because our two scanners are

of different field strengths, we entered scanner site as a nuisance

covariate in all analyses. Similar approaches to merging independent

component analysis (ICA)-based, network-oriented analysis of fMRI

task data across multiple scanners and field strengths have been

reported (Kim et al., 2009).

Image processing and analysis

Structural imaging

To obtain grey matter tissue probability maps for atrophy correction in

functional imaging analyses, T1-weighted magnetic resonance images

underwent an optimized voxel-based morphometry protocol (Good

et al., 2001) using Statistical Parametric Mapping-5 (http://www

.fil.ion.ucl.ac.uk/spm/). First, a study-specific template and tissue

priors were created to minimize spatial normalization and segmenta-

tion errors. This approach helps to identify group differences in

patients with neurodegenerative disease (Senjem et al., 2005). All sub-

jects (n = 36) were used to create the template, and custom images

for each subject were generated by applying affine and deformation

parameters obtained from normalizing the grey matter images,

segmented in native space, to the custom template. Modulation was

performed by multiplying voxel values by the Jacobian determinants

derived from the spatial normalization step, and the resulting

grey matter maps were smoothed with a 10 mm isotropic Gaussian

kernel.

Functional imaging

Preprocessing and ICN derivation

After discarding the first six frames to allow for magnetic field stabil-

ization, functional images were realigned and unwarped, slice-time

corrected, normalized and smoothed with a 4 mm full-width at

half-maximum Gaussian kernel using SPM5 (http://www.fil.ion.ucl

.ac.uk/spm/). Normalization was carried out by calculating the warp-

ing parameters between the mean T2* images and the Montreal

Neurological Institute echo planar imaging template and applying

them to all images in the sequence. Subsequently, the images were

re-sampled at a voxel size of 2 mm3.

After preprocessing, we used spatial probabilistic ICA to isolate ICN

maps following previous methods (Beckmann and Smith, 2004). ICA

decomposes a time course of whole-brain volumes (a 4D image) from

a single subject into independent spatiotemporal components and

consistently identifies low-frequency ICN patterns from data acquired

at various spatial and temporal resolutions (Damoiseaux et al., 2006).

Preprocessed images were concatenated into 4D files and entered

into FSL 4.0 Melodic ICA software (http://www.fmrib.ox.ac.uk/fsl/

index.html). We allowed the program to determine the dimensionality

of each data set automatically, including the number of components.

Across our 36 subjects, ICA extracted an average of 46.3 components

(range 28–60; SD 8.5). After removing any components in which

high-frequency signal (40.1 Hz) constituted 50% or more of the

power in the Fourier spectrum, an average of 31.9 components

(range 14–48; SD 8.1) remained. Next, we used an automated tem-

plate matching procedure to obtain subject-specific best-fit ICN maps

for the Salience Network and the DMN (Seeley et al., 2007b, 2009).

Goodness-of-fit was calculated by comparing each component from

each subject to binarized group ICA maps of the Salience Network and

DMN built from 15 healthy young subjects (ages 19–40; mean age,

26.5 years; nine females, all right-handed) from a separate dataset.

Details regarding this dataset and corresponding group ICA maps have

been published previously (Habas et al., 2009). These ICN templates

were thresholded at a z-score �4.0 to be visually comparable to the

consistent ICNs published by Damoiseaux et al. (2006). A minor

modification of previous goodness-of-fit methods (Seeley et al.,

2007b, 2009) was included here for template matching, with

goodness-of-fit scores calculated by multiplying (i) the average

z-score difference between voxels falling within the template and

voxels falling outside the template; and (ii) the difference in the

percentage of positive z-score voxels inside and outside the template.

This goodness-of-fit algorithm proves less vulnerable to inter-subject

variability in shape, size, location and strength of each ICN. Within the

selected ICA components, each voxel’s z-score represents the degree

to which that voxel’s time series correlates with the overall component

time series. Accordingly, significant group differences at each voxel

reflect focal connectivity reduction or enhancement relative to the

associated overall ICN.

Group differences in ICN strength

Random effects analyses were performed using each subject’s best-fit

component images and a ‘full factorial’ design implemented in SPM5

(Fig. 1). Two-sample t-tests were used to generate group difference

maps among control, Alzheimer’s disease and bvFTD, with scanner site

entered as a confounding covariate.

To determine whether observed group differences resulted from

underlying grey matter atrophy, we re-analysed each contrast after

adding voxel-wise grey matter probability maps as covariates and

scanner site as a confounding covariate using the Biological

Parametric Mapping toolbox (Casanova et al., 2007). Grey matter

probability maps derived from voxel-based morphometry were regis-

tered to the same standard image space as the functional images and

were re-sampled to equalize voxel sizes and image dimensions across

the functional and structural data.

To evaluate further whether and how observed group ICN differ-

ences were related to underlying grey matter atrophy, we calculated

the correlation between voxel-wise grey matter intensity maps and

ICN maps for each network for the 12 bvFTD and 12 Alzheimer’s

disease subjects, with scanner site entered as a confounding covariate,

using the Biological Parametric Mapping toolbox (Casanova et al.,

2007).

Correlations between ICN connectivity disruptions and

enhancements

Based on the anticorrelated relationship between the Salience Network

and DMN (Greicius et al., 2003; Greicius and Menon, 2004; Fox et al.,

2005; Seeley et al., 2007a), we further explored whether the identified

disease-related connectivity enhancements might correlate with

specific regions of reduced ICN strength in whichever network

(Salience Network or DMN) did not contain the enhancement. To

this end, we first generated 3 mm radius spherical regions of interest

centred on the most significant peaks of connectivity enhancement in

the group contrast results shown in Fig. 2A and B (second row): left

angular gyrus (–46, –66, 34) in the DMN bvFTD4controls contrast

(centring peak reprinted in Fig. 4A) and right pregenual anterior

cingulate cortex (12, 32, 30) in the Salience Network Alzheimer’s

1356 | Brain 2010: 133; 1352–1367 J. Zhou et al.

disease4controls contrast (centring peak reprinted in Fig. 4B). We

then extracted the mean ICA component z-scores from within the

DMN left angular gyrus region of interest for all bvFTD and control

subjects and entered these mean z-scores as covariates of interest in

voxel-wise Salience Network correlation analyses over all 24 subjects,

with scanner site entered as a nuisance covariate, seeking regions that

exhibited significant negative correlation with the left angular gyrus.

Because the Biological Parametric Mapping toolbox is not designed for

such correlation analyses, we generated atrophy-corrected results by

repeating these analyses after dividing each subject’s ICN map by their

grey matter map on a voxel-by-voxel basis (Matsuda et al., 2002). For

Alzheimer’s disease, we conducted a parallel analysis, using the same

design skeleton just detailed, seeking instead specific DMN connectiv-

ity reductions that correlated with the amplified Salience Network

right pregenual anterior cingulate cortex connectivity observed in

Alzheimer’s disease4controls.

ICN correlations with bvFTD clinical severity

Clinical severity was assessed using the CDR scale, sum of boxes score

(CDR-SB). We chose the CDR-SB because it represents a validated

global functional impairment measure, provides a relatively continuous

variable and proves sensitive to disease stage in bvFTD (Rosen et al.,

2004; Seeley, 2008). Voxel-wise regression analyses were conducted

to identify regions whose intrinsic connectivity in each network corre-

lated with CDR-SB. BvFTD and Alzheimer’s disease were assessed sep-

arately. We predicted that, in bvFTD, regions of worsening Salience

Network connectivity would correlate with increasing CDR-SB, but we

also tested for significant correlations (positive or negative) between

DMN connectivity and CDR-SB. Similar analyses were conducted for

Alzheimer’s disease, although we noted that the limited CDR-SB

dynamic range in the Alzheimer’s disease group would reduce the

likelihood of identifying significant correlations. CDR-SB was entered

as a covariate of interest, and scanner site was entered as a nuisance

covariate. To examine whether identified ICN correlations with

CDR-SB were explained by grey matter atrophy alone, we divided

each subject’s ICN map by their whole brain grey matter map

on a voxel-by-voxel basis and repeated the same analyses. To

evaluate the strength of the linear relationship between ICN con-

nectivity and clinical severity, we further performed linear regression

between CDR-SB and cluster-wise mean z-scores identified in

bvFTD.

Linear discriminant analysis of network summary scores

Because our main analyses confirmed that bvFTD and Alzheimer’s

disease feature divergent effects on the Salience Network and DMN,

we questioned whether a summary score incorporating both ICNs

might better differentiate bvFTD from Alzheimer’s disease and each

patient group from healthy controls. To explore this possibility, we

generated a ‘Salience Network minus DMN’ index using a four-step

procedure. First, to identify the most discriminating regions in this

dataset, we performed conjunction analyses on the group-level ICN

contrasts to identify regions significant in bvFTD5controls and

Alzheimer’s disease4controls for the Salience Network and in

Alzheimer’s disease5controls and bvFTD4controls for the DMN.

Second, we generated 3 mm radius spherical regions of interest

centred on those peaks identified in the Salience Network (14 regions)

and DMN (eight regions) conjunction analyses. Third, we extracted the

mean z-scores for all regions of interest from subjects’ Salience

Network and DMN best-fit ICA components. Finally, each subject’s

Salience Network minus DMN index was calculated by averaging

over all regions of interest in each ICN, subtracting the DMN average

from the Salience Network average and normalizing values across all

subjects.

Three-class (controls, bvFTD and Alzheimer’s disease) classification

using linear discriminant analysis was performed on the Salience minus

DMN index by leave-one-out cross-validation. We used the scores

from each of the 36 single subjects as the validation data for classifi-

cation, and the remaining 35 subjects as the training data to derive

linear discriminant models. This procedure was repeated iteratively

such that each subject was used once as the validation data, and

overall classification performance was calculated based on the

36 single-subject classification results.

Some patients with early age-of-onset dementia present with clinical

features falling between the typical bvFTD and Alzheimer’s disease

profile, leading to diagnostic uncertainty. To begin to assess how

well ICN-based classification might perform in this context, we

searched our database for patients who (i) met clinical diagnostic

research criteria for both bvFTD and Alzheimer’s disease; (ii) had

undergone an ICN fMRI scan; and (iii) had available pathology, a

known disease-causing mutation or a PIB-PET result. Four such

patients were identified, all of whom had positive PIB scans and no

other supporting data (Table 1). The ICN fMRI acquisition parameters

for these subjects were identical in two (one Stanford, one SFVAMC)

and featured a slightly longer repetition time in two subjects

(SFVAMC) to obtain more complete brain coverage. After performing

all preprocessing, ICA and component selection steps identically to the

36 subjects used to train our three-class classification algorithm, we

tested this algorithm on the four PIB-positive bvFTD versus Alzheimer’s

disease subjects.

Statistical thresholding for image analyses

A uniform strategy for statistical image thresholding was employed for

all primary analyses, including group-level ICN contrasts, correlations

between ICN connectivity disruptions and reciprocal ICN enhance-

ments, and ICN correlations with clinical severity. For these analyses

(atrophy uncorrected and corrected), we identified significant clusters

using a joint height and extent probability threshold of P50.05,

corrected at whole-brain level (Poline et al., 1997). The statistical

maps were masked explicitly to the relevant ICN by binarizing the

three-group average ICN at a height threshold of P50.001 (uncor-

rected) and including subcortical and brainstem structures (regardless

of membership in the three-group average ICN map) to avoid missing

focal effects in these finer-grained anatomical regions. The P50.001

mask threshold was chosen to constrain generously the search volume

for group ICN contrasts to regions that compose the relevant ICN

without including regions that make up other adjacent ICNs.

‘Supplementary Results’ regarding the correlation between ICN con-

nectivity and grey matter atrophy employed the same thresholding

procedures.

Single-subject ICN scores were derived by using conjunction

analyses to identify group-discriminating Salience Network and DMN

regions first. For these conjunction analyses, we applied a liberal height

threshold of P50.05 uncorrected, masked explicitly to the relevant

ICN by binarizing to voxels with positive z-scores in the three-group

average ICN map. This more liberal threshold served not to test a

specific hypothesis but to identify multiple discriminating regions for

incorporation into ICN scores.

The Automated Anatomical Labelling toolbox was used to iden-

tify the number of grey matter voxels in regions of interest

(Tzourio-Mazoyer et al., 2002). For display purposes, all statistical

maps are overlaid on a T1-weighted Montreal Neurological Institute

template using MRIcron (http://www.sph.sc.edu/comd/rorden/

mricron/).

Divergent connectivity in bvFTD and Alzheimer’s disease Brain 2010: 133; 1352–1367 | 1357

Statistical analysesGroup differences in measures displayed in Table 1 were assessed

using the Statistical Package for Social Sciences (SPSS v.17.0), employ-

ing analyses of variance (ANOVA) or �2 tests as appropriate. All ana-

lyses performed in SPSS were considered significant for values of

P� 0.05. To compare the discriminating value of the ICN summary

score to standard clinical measures, we also performed a parallel clas-

sification analysis on the eight most discriminating neuropsychological

measures, those performed on all three groups that featured at least

one significant pairwise group difference (Table 1). Three-class linear

discriminant analysis using leave-one-out cross-validation was

performed on those subjects for whom all discriminating measures

were available (7 healthy controls, 10 Alzheimer’s disease subjects

and 12 bvFTD). We also repeated the ICN-based classification analysis

on these same 29 subjects.

Results

BvFTD and Alzheimer’s disease featuredivergent Salience Network and DMNconnectivity changesBvFTD and Alzheimer’s disease present with contrasting deficit

and strength profiles and target networks known to adopt an

anticorrelated relationship in the healthy brain (Seeley et al.,

2007a). Accordingly, we questioned whether these disorders

would show opposing patterns of diminished and accentuated in-

trinsic connectivity within their signature large-scale networks

(Seeley et al., 2009). Group-level Salience Network and DMN

Figure 2 BvFTD and Alzheimer’s disease feature divergent Salience Network and DMN dynamics. Group difference maps illustrate

clusters of significantly reduced or increased connectivity for each ICN. In the Salience Network (A), patients with bvFTD showed

distributed connectivity reductions compared to healthy controls (HC) and patients with Alzheimer’s disease (AD), whereas patients with

Alzheimer’s disease showed increased connectivity in anterior cingulate cortex and ventral striatum compared to healthy controls. In the

DMN (B), patients with Alzheimer’s disease showed several connectivity impairments compared to healthy controls and patients with

bvFTD, whereas patients with bvFTD showed increased left angular gyrus connectivity. Patients with bvFTD and Alzheimer’s disease

further showed focal brainstem connectivity disruptions within their ‘released’ network (DMN for bvFTD, Salience Network for

Alzheimer’s disease). Results are displayed at a joint height and extent probability threshold of P50.05, corrected at the whole brain level.

Colour bars represent t-scores, and statistical maps are superimposed on the Montreal Neurological Institute template brain.

1358 | Brain 2010: 133; 1352–1367 J. Zhou et al.

contrasts (Fig. 2 and Supplementary Tables 1 and 2) revealed

connectivity reduction and enhancement patterns consistent with

our hypotheses. Compared to healthy controls, patients with

bvFTD featured decreased cortical Salience Network connectivity

in right frontoinsula, left dorsal anterior insula, right superior tem-

poral pole and bilateral mid-cingulate cortices (Fig. 2A and

Supplementary Table 1). Strikingly, patients with bvFTD further

showed extensive Salience Network connectivity disruptions in

subcortical, limbic and brainstem structures (detailed in Fig. 3),

including the nucleus accumbens and ventral striatopallidum, thal-

amus (in the vicinity of the parafasicular, paracentral and ventral

mediodorsal nuclei bilaterally), habenular complex, hypothalamus,

basolateral amygdala/anterior hippocampus, substantia nigra/

ventral tegmental area, parabrachial nucleus and pontine and

medullary regions. Within the DMN, however, patients

with bvFTD showed increased connectivity in left angular gyrus

(Fig. 2B and Supplementary Table 2). Conversely, patients with

Alzheimer’s disease showed reduced DMN connectivity compared

to controls in left retrosplenial cortex/lingual gyrus, left posterior

hippocampus, left cuneus and midbrain tegmentum, in the vicinity

of the dorsal raphe nucleus (Fig. 2B) but increased Salience

Network connectivity versus controls in bilateral anterior cingulate

cortex and left ventral striatum (Fig. 2A). When compared to each

other, patients with bvFTD and Alzheimer’s disease showed similar

but more extensive differences than observed in the contrasts

between each patient group and controls. That is, bvFTD

showed dramatic Salience Network connectivity reduction com-

pared to Alzheimer’s disease, whereas in Alzheimer’s disease the

DMN was markedly disconnected compared to bvFTD, especially

in medial and lateral parietal regions. Critically, in no region did

patients with bvFTD show increased Salience Network connectivity

compared to those with Alzheimer’s disease or controls

(Supplementary Table 1). Likewise, in patients with Alzheimer’s

disease, no regions showed increased DMN connectivity compared

to controls and only the dorsal caudate showed increased DMN

connectivity compared to patients with bvFTD (Supplementary

Table 2). Together, these findings suggest the following symmet-

rical patterns: Salience Network = bvFTD5controls5Alzheimer’s

disease and DMN = Alzheimer’s disease5controls5bvFTD, sup-

porting a ‘reciprocal networks’ model to explain the divergent

clinical and network features seen in these two disorders (Seeley

et al., 2007a).

Despite the observed ICN enhancements (bvFTD in DMN and

Alzheimer’s disease in Salience Network), we detected overlapping

focal midbrain, epithalamic and thalamic connectivity reductions in

each patient group compared to healthy controls within the

network showing cortical connectivity enhancement for that

group (Fig. 2A and B, bottom row). More specifically, in the

Salience Network Alzheimer’s disease showed reductions in

posterior hypothalamus and dorsal midbrain/periaqueductal grey

matter. Likewise, in the DMN bvFTD showed connectivity disrup-

tion in the mibrain tegmentum, habenular complex, tectum/

periaqueductal grey matter and the dorsomedial thalamus and

hypothalamus.

After atrophy correction, most regions remained significant

in all Salience Network contrasts (Supplementary Table 1

and Supplementary Fig. 1). In the DMN contrasts, all detected

regions remained significant after atrophy correction

(Supplementary Table 2 and Supplementary Fig. 1). To explore

the relationship between grey matter volume and ICN connectivity

further, we built correlation maps of these two measures across all

24 patients and compared these maps to the regions whose con-

nectivity reductions were affected by atrophy correction

(Supplementary Materials and Supplementary Fig. 1).

ICN enhancements relate to specificconnectivity disruptions within thereciprocal networkThe reciprocal networks model predicts that ICN enhancements

in one network may relate to specific disruptions in the other

(reciprocal) network. To test this idea, we sought Salience

Network regions, in bvFTD and controls, whose connectivity

strength correlated inversely with connectivity in the left angular

gyrus, a DMN region showing enhanced connectivity in bvFTD

(Fig. 2B). As highlighted in Fig. 4A, DMN left angular gyrus con-

nectivity was inversely correlated with Salience Network connect-

ivity in the right frontoinsula/putamen (peak = 56, 0, 8; t = 3.36;

cluster size = 337), anterior mid-cingulate cortex (peak = 6, �12,

54; t = 3.78; cluster size = 650) and bilateral dorsal pontine regions

Figure 3 Subcortical and brainstem Salience Network connectivity reductions in bvFTD. Statistical maps illustrate the Salience Network

contrast of bvFTD5controls. Axial slice insets are provided for selected brainstem regions to clarify cluster locations. blA = basolateral

nucleus of amygdala; Hc = habenular complex; PBN = parabrachial nucleus; Pc = paracentral nucleus of thalamus; Pf = parafasicular nucleus

of thalamus; RedN = red nucleus; SN = substantia nigra; vMD = ventral mediodorsal nuclei; VSP = ventral striatopallidum; VTA = ventral

tegmental area; PAG = periaqueductal grey matter. Presentation details otherwise as in Fig. 2.

Divergent connectivity in bvFTD and Alzheimer’s disease Brain 2010: 133; 1352–1367 | 1359

in the vicinity of the parabrachial nuclei (peak =�8, �28, �42;

t = 3.81; cluster size = 364). Similarly, Fig. 4B reveals that right

pregenual anterior cingulate cortex, the Salience Network region

enhanced in Alzheimer’s disease (Fig. 2A), correlated inversely, in

both Alzheimer’s disease and controls, with regions throughout

the DMN, including ventral medial prefrontal cortex (peak = 4,

64, 4; t = 4.57; cluster size = 1418), precuneus/posterior cingulate

cortex/cuneus (peak =�16, �74, 20; t = 4.23; cluster size = 2257),

left middle temporal gyrus (peak =�60, �18, �20; t = 4.04;

cluster size = 342) and right middle temporal gyrus (peak = 60,

�22, �30; t = 3.53; cluster size = 284). Scatterplots in Fig. 4

illustrate the relative contributions of the patient and control

groups to each reciprocal network relationship. Overall, these

findings suggest that specific connectivity disruptions within

each network may enhance connectivity within the reciprocal

network.

Salience Network disruption andDMN enhancement correlate with theclinical severity of bvFTDHaving demonstrated Salience Network connectivity disruptions

and DMN connectivity enhancements in bvFTD, we sought

those regions in each network that significantly correlated with

bvFTD clinical severity after atrophy correction. As illustrated in

Fig. 5A, CDR-SB correlated with selective connectivity reduction

in the right frontoinsula (peak = 46, 20, �2; t = 6.50; cluster

size = 712). In contrast, clinical severity showed a positive correla-

tion with DMN connectivity, including left angular gyrus

(peak = (�58, �68, 22; t = 3.96; cluster size = 313), right angular

gyrus (peak = 50, �48, 12; t = 4.82; cluster size = 287), ventral

medial prefrontal cortex (peak = 8, 58, 12; t = 9.49; cluster

size = 720) and right intraparietal sulcus (peak = 30, �76, 30;

t = 5.41; cluster size = 173) (Fig. 5B). R-square coefficients with

respect to CDR-SB were computed from raw mean cluster-wise

ICN z-scores, defined in atrophy-corrected models (Fig. 5). In the

reverse direction, no regions negatively correlated with bvFTD

clinical severity in the DMN, and only one region positively corre-

lated with clinical severity in the Salience Network, but the

majority of this cluster was rooted in deep subfrontal white

matter. No correlations between CDR-SB and Salience Network

or DMN connectivity were detected in Alzheimer’s disease, most

likely due to the limited CDR-SB dynamic range in this group.

Three-class classification of controls,bvFTD and Alzheimer’s disease basedon intrinsic connectivityTo evaluate the potential of ICN mapping to differentiate between

controls, patients with bvFTD and patients with Alzheimer’s

Figure 4 ICN enhancements relate to specific connectivity disruptions within the reciprocal networks. (A) Left angular gyrus (L ANG)

DMN connectivity was intensified in patients with bvFTD versus healthy controls and the 3 mm radius spherical region of interest centred

on its peak showed a significant inverse correlation with SN connectivity in right frontoinsula (R FI), anterior mid-cingulate cortex (aMCC)

and the dorsal pons (not shown) across patients with bvFTD and healthy controls (HC). Group-wise scatterplots illustrate the relative

contribution of each group to these relationships for each anticorrelated region. (B) Right pregenual anterior cingulate cortex (R pACC) SN

connectivity, enhanced in patients with Alzheimer’s disease (AD), showed a significant inverse relationship with DMN connectivity in

precuneus (PreCu), ventromedial prefrontal cortex (vmPFC) and bilateral middle temporal gyrus (not shown) across patients with

Alzheimer’s disease and healthy controls. Results are atrophy-corrected and thresholded at a joint height and extent probability of

P50.05, corrected at the whole brain level. Colour bars represent t-scores, and statistical maps are superimposed on the Montreal

Neurological Institute template brain.

1360 | Brain 2010: 133; 1352–1367 J. Zhou et al.

disease, we identified the most group-discriminating regions in our

dataset for each ICN (listed for each network in Supplementary

Table 2) and derived each subject’s corresponding Salience

Network score, DMN score, and a combined index of Salience

Network minus DMN connectivity. Grouped scatter plots (Fig. 6)

revealed that combining Salience Network and DMN scores

achieved the best separation among the three groups and 100%

separation between bvFTD and Alzheimer’s disease. Linear

discriminant analyses of three-class classification based on the

combined Salience Network minus DMN index confirmed the util-

ity of this technique. Table 2 summarizes the classification results

of the leave-one-out cross-validation. ICN analysis achieved 92%

total three-class classification accuracy, as well as excellent sensi-

tivity and specificity for bvFTD and Alzheimer’s disease.

Neuropathological assessment remains the ideal gold standard for

dementia diagnostic biomarker development. Novel biomarkers

Figure 5 Salience Network disruption and DMN enhancement correlate with bvFTD clinical severity. In bvFTD, CDR, sum of boxes scores

(CDR-SB) showed a significant (A) negative correlation with Salience Network connectivity in right frontoinsula (FI) and (B) positive

correlation with DMN connectivity in bilateral angular gyrus (ANG), as well as ventromedial prefrontal cortex (vmPFC) and right

intraparietal sulcus (R IPS, not shown). Results are atrophy corrected and thresholded at a joint height and extent probability of P50.05,

corrected at the whole brain level. Colour bars represent t-scores, and statistical maps are superimposed on the Montreal Neurological

Institute template brain.

Figure 6 Group discrimination using Salience Network and DMN metrics. (A) Scatterplot of Salience Network (SN) scores of healthy

controls (HC), patients with bvFTD and Alzheimer’s disease (AD) with respect to clinical diagnosis. Each subject’s Salience Network score

represents the average z-score across fourteen regions of interest derived from a Salience Network (bvFTD5healthy control) +

(Alzheimer’s disease4healthy control) conjunction analysis (Supplementary Table 3). (B) DMN scores were computed in a parallel fashion,

using eight regions of interest derived from a DMN (Alzheimer’s disease5healthy control) + (bvFTD4healthy control) conjunction

analysis (Supplementary Table 3). (C) The combined Salience Network minus DMN index provided superior group discrimination

compared to analysis of either network alone. Dot columns were converted to clouds by random number assignment within each group

to improve scatter visualization. Key for colours and shapes is provided at the bottom.

Divergent connectivity in bvFTD and Alzheimer’s disease Brain 2010: 133; 1352–1367 | 1361

like ICN mapping, however, must often be assessed without

neuropathological confirmation due to the typical interval

(5–8 years) between diagnosis and death. Therefore, to add

weight to the clinical diagnoses that served as entry criteria for

this study, we gathered all supportive biological data available for

this series and included these data in Fig. 6. Although unknown to

clinicians at the time of diagnosis, these data supported the clinical

diagnoses without exception. Patients with biological support

were spread throughout their respective group distributions for

each ICN measure.

To compare the discriminating value of the Salience Network

minus DMN score to standard clinical measures, we further

performed a three-class classification analysis on the eight most

discriminating neuropsychological measures for a subset of our

subjects for whom these measures were uniformly available

(7 normal controls, 10 Alzheimer’s disease and 12 bvFTD). This

classification achieved 66% accuracy. In contrast, Salience

Network minus DMN scores achieved 90% accuracy when used

to classify the same 29 subjects.

Finally, we explored whether the ICN algorithm might help to

resolve diagnostic controversies between bvFTD and Alzheimer’s

disease. Four patients with multi-domain cognitive impairment and

severe behavioural symptoms (Table 1) met clinical research

criteria for both disorders but showed extensive amyloid binding

on PIB-PET imaging, most consistent with a pathological diagnosis

of Alzheimer’s disease. These patients, though not used to train

the ICN algorithm, fell within the low end of the Alzheimer’s

disease Salience Network minus DMN distribution, well above

the bvFTD range. Three of four subjects were categorized as

Alzheimer’s disease in three-class classification (one misclassified

as a healthy control), but, more importantly, all four were

categorized as Alzheimer’s disease in two-class (bvFTD versus

Alzheimer’s disease) classification, corresponding well with the

PIB-PET results. Just as patients with Alzheimer’s disease may

have significant behavioural symptoms, a subset with bvFTD,

especially those with progranulin mutations (Rohrer et al., 2008)

shows prominent parietal deficits, which could undermine

ICN-based classification. In this study, the one patient with a

progranulin mutation showed severe parietal involvement

(Supplementary Fig. 2) but still fell safely within the bvFTD ICN

spectrum (Fig. 6).

DiscussionWe used task-free ICN fMRI to show that bvFTD and Alzheimer’s

disease result in divergent, indeed opposing patterns of large-scale

network connectivity. Although previous studies have demon-

strated ICN disruptions in Alzheimer’s disease (Greicius et al.,

2004; Rombouts et al., 2005; He et al., 2007; Sorg et al.,

2007; Fleisher et al., 2009) and other neurodegenerative disorders

(Mohammadi et al., 2009; Wu et al., 2009), this study is the first

to apply ICN analysis to bvFTD, or to compare two neurodegen-

erative diseases with each other. Consistent with our hypotheses,

bvFTD undermined the Salience Network, a distributed anterior

ICN dedicated to social-emotional and visceroautonomic process-

ing (Seeley et al., 2007b; Taylor et al., 2009), but intensified con-

nectivity within a posterior DMN involved in episodic memory and

visuospatial functions (Greicius et al., 2003; Buckner et al., 2005).

Alzheimer’s disease, conversely, showed DMN reductions but

Salience Network enhancements. These reciprocal findings parallel

the clinical deficit-strength profiles in bvFTD and Alzheimer’s dis-

ease (Seeley et al., 2007a) and the Salience Network-DMN

anti-correlation relationship in the healthy brain (Greicius et al.,

2003; Fox et al., 2005; Fransson, 2005; Seeley et al., 2007a).

Furthermore, specific ‘disconnections’ within each disease-targeted

network predicted ICN enhancements within the reciprocal net-

work. Congruently, advancing bvFTD severity correlated with

reduced Salience Network connectivity in the right frontoinsula

but with enhanced biparietal DMN connectivity. Focal atrophy,

especially when severe, accentuated connectivity reductions in

bvFTD, but voxel-wise atrophy correction methods also confirmed

that most ICN group differences could not be explained by atro-

phy alone. To explore ICN mapping as a diagnostic biomarker, we

identified group-discriminating ICN changes in this dataset and

used them to achieve 92% overall three-group diagnostic classifi-

cation accuracy and 100% separation between bvFTD and

Alzheimer’s disease, even among patients with tenuous clinical

diagnoses. The findings suggest that ICN assays, further de-

veloped, could become powerful tools in translational neurosci-

ence research.

Reciprocal networks: bvFTD andAlzheimer’s disease cause opposingICN alterations

Salience Network

Early bvFTD disrupts complex social-emotional functions, including

self-conscious emotion (Sturm et al., 2006, 2008), theory of mind

(Eslinger et al., 2005; Lough et al., 2006), empathy (Rankin et al.,

2005) and emotional morality (Mendez and Shapira, 2009). These

deficits arise in parallel with early (Broe et al., 2003; Seeley et al.,

2008a), consistent (Schroeter et al., 2008) and progressive

(Brambati et al., 2007) atrophy within Salience Network affiliated

regions. We detailed the Salience Network in healthy young

(Seeley et al., 2007b) and older (Seeley et al., 2009) controls, in

part to provide an anatomical roadmap for bvFTD investigation. In

this study, we discovered that bvFTD interrupts Salience Network

Table 2 Healthy controls, bvFTD and Alzheimer’s diseasethree-class classification accuracy of Salience Networkminus DMN scores by leave-one-out cross-validation

Predicted group

Group Healthycontrols

bvFTD Alzheimer’sdisease

Total

Healthy controls 11 1 0 12

bvFTD 0 12 0 12

Alzheimer’s disease 2 0 10 12

Sensitivity (%) 91.7 100 83.3 91.7

Specificity (%) 91.7 95.8 100 95.8

1362 | Brain 2010: 133; 1352–1367 J. Zhou et al.

connectivity, most notably in the frontal and anterior insula, mid-

cingulate and numerous subcortical, limbic and brainstem nodes

with known anatomical connectivity to Salience Network cortical

regions in primates (Mesulam and Mufson, 1982; Ongur and

Price, 2000; Saper, 2002; Heimer and Van Hoesen, 2006). The

right frontoinsula, in particular, may serve as a critical Salience

Network hub (Sridharan et al., 2008) that anchors subjective

awareness through multichannel integration of contextual (tem-

poral pole), hedonic (nucleus accumbens, ventral striatopallidum),

homoeostatic (amygdala, hypothalamus) and interoceptive

(parabrachial nucleus) processing streams (Damasio, 2003; Craig,

2009a, b). In early bvFTD, distributed Salience Network connect-

ivity reductions may disintegrate these complex visceroautonomic

and social-emotional signals, resulting in degraded self-

representations and impaired social behaviour.

Although patient strengths are rarely cited as important demen-

tia diagnostic clues (Miller et al., 2000), preserved social graces

and interpersonal warmth often lead experienced clinicians to

suspect Alzheimer’s disease in a patient with mild memory or

visuospatial complaints. Remarkably, we found that Alzheimer’s

disease produces heightened Salience Network connectivity in an-

terior cingulate cortex and ventral striatum compared with healthy

elderly controls. But what evidence supports Salience Network

functional gains in Alzheimer’s disease? Often, early-stage patients

exhibit heightened emotional sensitivity to others and to their own

cognitive deficits, avidly seeking eye contact with their caregivers,

and even patients with advanced disease remain attuned

to non-verbal emotional communication (Belgrave, 2009).

Behavioural symptoms, when present, often involve early emo-

tional sensitizations such as irritability and anxiety (Mega et al.,

1996; Benoit et al., 1999), the latter previously linked to height-

ened Salience Network anterior cingulate cortex connectivity in

healthy young subjects (Seeley et al., 2007b). In experimental

settings, patients with Alzheimer’s disease show preserved sensi-

tivity to salient social-emotional cues (Rankin et al., 2006; Mendez

and Shapira, 2009) and exaggerated anterior cingulate cortex

responses to nocioceptive stimuli (Cole et al., 2006). A previous

graph theoretical analysis of a different ICN fMRI dataset showed

frontal intrinsic connectivity enhancements in Alzheimer’s disease

(Supekar et al., 2008), including heightened frontostriatal connect-

ivity. The anterior cingulate cortex and frontoinsula, moreover,

stand out as the sole regions to show increased grey matter

volume in pathologically diagnosed Alzheimer’s disease compared

to frontotemporal lobar degeneration (Rabinovici et al., 2007b).

Like patients with Alzheimer’s disease, children with Williams

Syndrome, a neurodevelopmental disorder resulting from a

chromosome 7q11.23 microdeletion, show heightened sociality,

trait anxiety and severe parietal visuoconstructive deficits

(Meyer-Lindenberg et al., 2006), further supporting a reciprocal

relationship between anterior social and posterior spatial networks.

In the present study, heightened Salience Network connectivity in

Alzheimer’s disease was associated with reduced connectivity

throughout the DMN, suggesting that progressive DMN impair-

ment may shift Salience Network responses from sensitive to

oversensitive, resulting in worsening anxiety and agitation with

advancing disease. Future studies could directly assess whether

Salience Network connectivity enhancements explain the striking

social-emotional sensitivity, sometimes overflowing towards

anxiety, seen in patients with Alzheimer’s disease.

Default Mode Network

Early Alzheimer’s disease-related memory lapses, navigational

errors, and disrupted visuoconstructive processing may relate

to specific focal disruptions in posterior temporal and parietal cir-

cuits identified in this study. We further report Alzheimer’s

disease-related DMN connectivity reduction in or near the dorsal

raphe nucleus, which projects extensively to the medial temporal

lobe and represents an early (Rub et al., 2000), if not the earliest

(Grinberg et al., 2009), site of Alzheimer’s disease-related tau

neurofibrillary pathology. These clinicoanatomical deficits contrast

sharply with bvFTD, which typically spares (Mendez et al., 1996)

or, less often, intensifies (Miller et al., 1996, 2000; Seeley et al.,

2007a) visuospatial interest and ability. In one patient with

progressive non-fluent aphasia and focal left frontal operculo-

insular-striatal damage, we demonstrated increased right parietal

and temporal grey matter volume and perfusion, suggesting a

liberation of contralateral posterior cortical function (Seeley

et al., 2008b). Like patients with frontotemporal dementia, chil-

dren with autism, who feature social-emotional and anatomical

deficits akin to bvFTD (Di Martino et al., 2009a), may show

superior posterior cortical functions manifesting as extraordinary

artistic, arithmetic or mnestic talent (Hou et al., 2000; Treffert,

2009). In the present study, bvFTD showed increased left parietal

DMN connectivity that correlated with reduced Salience Network

connectivity in right frontoinsular, striatal and cingulate regions.

Intriguingly, a recent study demonstrated divergent frontotem-

poral dementia versus Alzheimer’s disease connectivity results

using graph theoretical analysis of resting-state EEG data

(de Haan et al., 2009). These authors showed that whereas

Alzheimer’s disease deviated from an optimal ‘small-world’

network structure toward a more random configuration, fronto-

temporal dementia showed an opposite trend toward a (perhaps

excessively) ordered structure, especially within the posterior alpha

rhythm. Although EEG lacks sufficient spatial resolution to interro-

gate specific anatomical networks, especially those (like the

Salience Network) composed of subcortical and deep cortical

structures, the more orderly posterior cortical covariance structure

in frontotemporal dementia may represent an electrophysiological

counterpart, at higher temporal resolution, to our ICN fMRI

findings.

Relationship between ICN changes anddisease severityTo assess whether ICN mapping might hold promise as a

disease-monitoring biomarker, we sought correlations between

regional ICN strength and bvFTD clinical severity. The results

provided a striking network dissociation. Whereas worsening

Salience Network right frontoinsula connectivity predicted greater

disease severity, so too did increases in bilateral angular gyrus

(ANG) connectivity within the DMN. These findings emphasize

the centrality of right frontoinsula in anchoring the Salience

Network (Seeley et al., 2007b; Sridharan et al., 2008) and in

giving rise, when injured, to bvFTD functional deficits. The right

Divergent connectivity in bvFTD and Alzheimer’s disease Brain 2010: 133; 1352–1367 | 1363

frontoinsula features the peak brain-wide concentration of von

Economo neurons, large bipolar Layer V projection neurons

shown to undergo early, selective degeneration in bvFTD but

not in Alzheimer’s disease (Seeley et al., 2006, 2007a).

Consistent with the present ICN findings, we recently found that

selective von Economo neuron loss in the right (but not left) fron-

toinsula correlated with bvFTD clinical severity measured with the

CDR-SB (Kim et al., unpublished results). Sensitive, subject-level

tracking of disease stage represents a major potential advantage of

ICN fMRI over structural imaging, which may lag too far behind

dynamic clinical and biological changes to prove useful for drug

development.

ICN mapping as a diagnostic biomarkerfor neurodegenerative diseaseWe combined Salience Network and DMN connectivity scores to

classify our three groups with 92% accuracy and separate bvFTD

and Alzheimer’s disease with 100% accuracy based on clinical

diagnosis. This approach requires replication in an independent

clinical dataset and validation in pathologically verified clinical

samples. Available supportive biological data, however, strongly

supported clinical diagnoses used to group patients in this study.

Because other frontotemporal dementia syndromes, such as

semantic dementia, progressive non-fluent aphasia and cor-

ticobasal syndrome also cause atrophy within specific ICNs

(Seeley et al., 2009), the present results warrant future direct

ICN investigations of these disorders.

Reliable discrimination of frontotemporal dementia syndromes

from Alzheimer’s disease will become critical as disease modifying

treatments emerge. Some patients present with prominent behav-

ioural but also memory and spatial impairments, leading clinicians

to waver diagnostically between bvFTD and Alzheimer’s disease.

Previous attempts to separate these disorders with imaging have

yielded mixed results, and to our knowledge no previous method

has provided 100% separation. Recently, a complex grey matter

spatial pattern analysis achieved 100% separation of Alzheimer’s

disease or frontotemporal dementia from controls, but accuracy

fell to 84% when applied to frontotemporal dementia versus

Alzheimer’s disease (Davatzikos et al., 2008). Machine learning

algorithms performed on single photon emission computed tom-

ography images provided 88% accuracy for frontotemporal

dementia versus Alzheimer’s disease (Horn et al., 2009). In the

largest study to include a pathological gold standard, Foster et al.

(2007) used PET with fluorodeoxyglucose (FDG-PET) to achieve

90% diagnostic accuracy for frontotemporal dementia versus

Alzheimer’s disease. In the present study, patients with typical

bvFTD and Alzheimer’s disease were classified with 100% accur-

acy, and four diagnostically controversial PIB-positive patients not

used to train the algorithm were classified as Alzheimer’s disease

by our approach.

ICN fMRI biomarkers could provide several important advan-

tages over existing functional-metabolic (FDG-PET) and molecular

(PIB-PET) imaging methods. Though both PET techniques show

promise for separating frontotemporal dementia from

Alzheimer’s disease (Foster et al., 2007; Rabinovici et al.,

2007a), they remain invasive and costly, available only at selected

centres, and repeatable only after a prolonged interval due to

radioactivity exposure concerns. Furthermore, PET may lack the

spatial resolution to detect focal limbic, subcortical or brainstem

dysfunction and PIB-PET has failed to track clinical severity in pa-

tients with Alzheimer’s disease (Scheinin et al., 2009). In contrast,

ICN fMRI provides a simple, non-invasive, inexpensive technique

that adds less than 10 min to a standard clinical MRI scan routinely

obtained to rule out non-degenerative structural disease.

Furthermore, ICN fMRI provides an immediately repeatable and

treatment-responsive (James et al., 2009) method which, as

shown in this study, proves sensitive to disease severity and may

help discriminate between bvFTD and Alzheimer’s disease. Finally,

the task-free nature of ICN fMRI mitigates concerns relevant to

implementing scanner-based fMRI tasks in patients with poor

working memory, apathy or behavioural impersistence. No doubt

owing to these issues, only one small task-based fMRI study of

bvFTD has been published to date (Rombouts et al., 2003). As

ICN biomarker development moves forward, further information

will be needed regarding how pharmacological conditions (scan-

associated sedation or ongoing treatments) impact ICN strength.

Limitations and future directionsThis study’s major limitations relate to sample size, scanner

variability and the lack of a large independent dataset to test

our exploratory diagnostic classification algorithm. Twelve subjects

per group, though sufficient to detect hypothesized group differ-

ences, proved too few to explore correlations between ICN

strength and specific behavioural or neuropsychological variables

of interest; we hope to pursue these goals in future studies. We

accounted for scanner variability by matching groups for scan site

and by including scan site as a confounding covariate in all

analyses, following previous approaches (Kim et al., 2009).

Greatest caution should be applied to our diagnostic algorithm,

which was initially trained and tested on the same subject pool,

making it unlikely to perform as well on an independent clinical

sample. Although the accurate classification of four diagnostically

challenging independent ‘test’ patients is encouraging, future

larger studies are needed to delineate the most discriminating

and generalizable indices of ICN function and to confirm that

ICNs prove reliable within subjects, as suggested by several

recent studies (Fox et al., 2007; Cohen et al., 2008; Meindl

et al., 2009; Shehzad et al., 2009). Increasingly advanced

data-driven methods may help speed researchers towards these

goals.

AcknowledgementsThe authors thank Richard C. Crawford, Katherine Prater,

Victor Laluz, Christopher Ching and Rudolph Walter for technical

assistance with data collection and storage, William J. Jagust for

PIB-PET imaging, and A.D. (Bud) Craig for discussion. Finally, we

thank our patients and their families for their generous contribu-

tions to neurodegeneration research.

1364 | Brain 2010: 133; 1352–1367 J. Zhou et al.

FundingNational Institute of Aging (NIA grant number K08 AG027086

to W.W.S., P01 AG19724 and P50 AG1657303-75271 to

B.L.M., R01-AG027859 to William J. Jagust, and K23-AG031861

to G.D.R.); the National Institute of Neurological Disorders and

Stroke (NINDS grant number K23NS048302 to M.D.G.);

Alzheimer’s Association (grant number NIRG-07-59422 to

G.D.R); and the Larry L. Hillblom Foundation (grant number

2005/2T to W.W.S.).

Supplementary materialSupplementary material is available at Brain online.

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